Robust short-term prediction of wind power generation under uncertainty via statistical interpretation of multiple forecasting models

Abstract The predictions of the autoregressive moving average model, the artificial neural network model, and the grey prediction model are comparatively studied for the wind power generation. Each predictive model is the most suitable for a certain variance of wind at a given period. In this study, the weighting method is proposed to systematically combine the predicted values of those three predictive models over time, based on their forecasting performance by the root mean square errors (RMSEs) between the actual values and the predicted values. The multiple forecasting models are applied to predict the wind power generation of a wind farm with 1 h, 3 h and 6 h ahead. The RMSEs of the multiple forecasting models are significantly the lowest values among those three predictive models and the benchmark of the persistence model. Also, the prediction interval around the predicted value is statistically determined to indicate the feasible range of the wind power generation with a prescribed percentage of confidence under uncertainty causing the historic prediction errors.

[1]  Peng Guo,et al.  A Review of Wind Power Forecasting Models , 2011 .

[2]  D. Marquardt.  An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[3]  David Barbosa de Alencar,et al.  Different Models for Forecasting Wind Power Generation: Case Study , 2017 .

[4]  R. Kavasseri,et al.  Day-ahead wind speed forecasting using f-ARIMA models , 2009 .

[5]  Richard A. Davis,et al.  Maximum likelihood estimation for noncausal autoregressive processes , 1991 .

[6]  Xiaoxia Qi,et al.  Deep belief network based k-means cluster approach for short-term wind power forecasting , 2018, Energy.

[7]  J. Deng,et al.  Introduction to Grey system theory , 1989 .

[8]  Xinxin Zhu,et al.  Short-Term Spatio-Temporal Wind Power Forecast in Robust Look-ahead Power System Dispatch , 2014, IEEE Transactions on Smart Grid.

[9]  Thananchai Leephakpreeda,et al.  Assessment of wind energy potential for selecting wind turbines: An application to Thailand , 2015 .

[10]  Natapol Korprasertsak,et al.  Nyquist-based adaptive sampling rate for wind measurement under varying wind conditions , 2018 .

[11]  J. M. Bates,et al.  The Combination of Forecasts , 1969 .

[12]  R.W. Dunn,et al.  One-hour-ahead wind speed prediction using a Bayesian methodology , 2006, 2006 IEEE Power Engineering Society General Meeting.

[13]  E. El-Saadany,et al.  Grey predictor for wind energy conversion systems output power prediction , 2006, IEEE Transactions on Power Systems.

[14]  Nadarajah Mithulananthan,et al.  Wind energy in Thailand to enhance energy security: Potential, status and barriers , 2008 .

[16]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[17]  J. Torres,et al.  Forecast of hourly average wind speed with ARMA models in Navarre (Spain) , 2005 .

[18]  T. Evgeniou,et al.  To combine or not to combine: selecting among forecasts and their combinations , 2005 .

[19]  Natapol Korprasertsak,et al.  Optimal Design of Wind Boosters for Low Speed Vertical Axis Wind Turbines , 2015 .

[20]  Ergin Erdem,et al.  ARMA based approaches for forecasting the tuple of wind speed and direction , 2011 .

[21]  Wen-Yeau Chang,et al.  A Literature Review of Wind Forecasting Methods , 2014 .

[22]  Jianzhou Wang,et al.  Short-term wind speed forecasting using a hybrid model , 2017 .

[23]  Cong Wang,et al.  A new wind power prediction method based on chaotic theory and Bernstein Neural Network , 2016 .

[24]  Jim Duggan,et al.  Forecasting energy demand, wind generation and carbon dioxide emissions in Ireland using evolutionary neural networks , 2018, Energy.

[25]  Heping Liu,et al.  Prediction of wind speed time series using modified Taylor Kriging method , 2010 .

[26]  Ali Lahouar,et al.  Hour-ahead wind power forecast based on random forests , 2017 .

[27]  Henrik Lund,et al.  Large-scale integration of wind power into different energy systems , 2005 .

[28]  Thananchai Leephakpreeda,et al.  Neural computing thermal comfort index for HVAC systems , 2005 .

[29]  Jianzhou Wang,et al.  Short-term wind speed prediction using empirical wavelet transform and Gaussian process regression , 2015 .

[30]  Huei-Lin Chang,et al.  Hour-ahead wind power and speed forecasting using simultaneous perturbation stochastic approximation (SPSA) algorithm and neural network with fuzzy inputs , 2010 .

[31]  Kamaladdin Fataliyev,et al.  One-step and multi-step ahead stock prediction using backpropagation neural networks , 2013, 2013 9th International Conference on Information, Communications & Signal Processing.

[32]  Okyay Kaynak,et al.  Grey system theory-based models in time series prediction , 2010, Expert Syst. Appl..

[33]  Nils Lid Hjort,et al.  Model Selection and Model Averaging , 2001 .

[34]  Kenneth Levenberg.  A METHOD FOR THE SOLUTION OF CERTAIN NON – LINEAR PROBLEMS IN LEAST SQUARES , 1944 .

[35]  Mustafa Jahangoshai Rezaee,et al.  Short-term power output forecasting of hourly operation in power plant based on climate factors and effects of wind direction and wind speed , 2018 .

[36]  Jin Hur,et al.  Short-term probabilistic forecasting of wind energy resources using the enhanced ensemble method , 2017, Energy.

[37]  Thananchai Leephakpreeda,et al.  Grey prediction on indoor comfort temperature for HVAC systems , 2008, Expert Syst. Appl..

[38]  Farshid Keynia,et al.  Short-term wind power forecasting using ridgelet neural network , 2011 .